Standardizing your data

For many machine learning algorithms, performance is highly sensitive to the relative scale of features. For that reason, it is often important to standardize your features. To standardize a feature means to shift all of its values so that their mean = 0 and to scale them so that their variance = 1.

One instance when normalizing is useful is when featuring the PE header of a file. The PE header contains extremely large values (for example, the SizeOfInitializedData field) and also very small ones (for example, the number of sections). For certain ML models, such as neural networks, the large discrepancy in magnitude between features can reduce performance.